Rhetoribert

🚀 sentence-transformers/static-retrieval-mrl-en-v1

このモデルは、sci_gen_colbert_triplets データセットを使用して、nomic-ai/nomic-embed-text-v1.5 からファインチューニングされた sentence-transformers モデルです。学術テキストの文章を、修辞的な機能（結果の要約、制限の表現など）に基づいて768次元の密ベクトル空間にマッピングし、機能的なテキストの類似性、制限分析、修辞的機能分類、クラスタリングなどに使用できます。

🚀 クイックスタート

モデルの詳細

モデルの説明

モデルのソース

• ドキュメント: Sentence Transformers Documentation
• リポジトリ: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

完全なモデルアーキテクチャ

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

使い方

直接使用（Sentence Transformers）

まず、Sentence Transformersライブラリをインストールします。

pip install -U sentence-transformers

次に、このモデルをロードして推論を実行できます。

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("KaiserML/RhetoriBERT")
# Run inference
sentences = [
    'Surveys and interviews: Introducing excerpts from interview data',
    "Through surveys and interviews, multiliterate teachers expressed a shared belief in the importance of fostering students' ability to navigate multiple discourse communities.",
    'The authors employ a constructivist approach to learning, where students build knowledge through active engagement with multimedia texts and collaborative discussions.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

評価

メトリクス

情報検索

• データセット: SciGen-Eval-Set
• InformationRetrievalEvaluator で評価

学習の詳細

学習データセット

sci_gen_colbert_triplets

• データセット: sci_gen_colbert_triplets at 44071bd
• サイズ: 35,934個の学習サンプル
• 列: query、positive、negative
• 最初の1000サンプルに基づく概算統計:

  	query	positive	negative
  タイプ	文字列	文字列	文字列
  詳細	最小: 5トークン 平均: 10.24トークン 最大: 23トークン	最小: 2トークン 平均: 39.86トークン 最大: 80トークン	最小: 18トークン 平均: 40.41トークン 最大: 88トークン

• サンプル:

  query	positive	negative
  Previous research: highlighting negative outcomes	Despite the widespread use of seniority-based wage systems in labor contracts, previous research has highlighted their negative outcomes, such as inefficiencies and demotivating effects on workers.	This paper, published in 1974, was among the first to establish the importance of rank-order tournaments as optimal labor contracts in microeconomics.
  Synthesising sources: contrasting evidence or ideas	Despite the observed chronic enterocolitis in Interleukin-10-deficient mice, some studies suggest that this cytokine plays a protective role in intestinal inflammation in humans (Kurimoto et al., 2001).	Chronic enterocolitis developed in Interleukin-10-deficient mice, characterized by inflammatory cell infiltration, epithelial damage, and increased production of pro-inflammatory cytokines.
  Previous research: Approaches taken	Previous research on measuring patient-relevant outcomes in osteoarthritis has primarily relied on self-reported measures, such as the Western Ontario and McMaster Universities Arthritis Index (WOMAC) (Bellamy et al., 1988).	The WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index) questionnaire has been widely used in physical therapy research to assess the impact of antirheumatic drug therapy on patient-reported outcomes in individuals with hip or knee osteoarthritis.

• 損失関数: MatryoshkaLoss で、以下のパラメータを使用:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          384,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

評価データセット

sci_gen_colbert_triplets

• データセット: sci_gen_colbert_triplets at 44071bd
• サイズ: 4,492個の評価サンプル
• 列: query、positive、negative
• 最初の1000サンプルに基づく概算統計:

  	query	positive	negative
  タイプ	文字列	文字列	文字列
  詳細	最小: 5トークン 平均: 10.23トークン 最大: 23トークン	最小: 18トークン 平均: 39.83トークン 最大: 84トークン	最小: 8トークン 平均: 39.89トークン 最大: 84トークン

• サンプル:

  query	positive	negative
  Providing background information: reference to the purpose of the study	This study aimed to investigate the impact of socioeconomic status on child development, specifically focusing on cognitive, language, and social-emotional domains.	Children from high socioeconomic status families showed significantly higher IQ scores (M = 112.5, SD = 5.6) compared to children from low socioeconomic status families (M = 104.3, SD = 6.2) in the verbal IQ subtest.
  Providing background information: reference to the literature	According to previous studies using WinGX suite for small-molecule single-crystal crystallography, the optimization of crystal structures leads to improved accuracy in determining atomic coordinates.	This paper describes the WinGX suite, a powerful tool for small-molecule single-crystal crystallography that significantly advances the field of crystallography by streamlining data collection and analysis.
  General comments on the relevant literature	Polymer brushes have gained significant attention in the field of polymer science due to their unique properties, such as controlled thickness, high surface density, and tunable interfacial properties.	Despite previous reports suggesting that polymer brushes with short grafting densities exhibit poorer performance in terms of adhesion and stability compared to those with higher grafting densities (Liu et al., 2010), our results indicate that the oppos

📄 ライセンス

このモデルは、apache-2.0ライセンスの下で提供されています。